Defects in printed images can be caused by a number of factors including anomalies in print medium, interactions between print medium and marking material, systematic defects introduced by print mechanisms or human error. Image defects may include but not be limited to scratches, spots, missing dot clusters, streaks, and banding.
Print defects are undesirable and efforts have been made in the art to develop suitable methods for their detection. Such techniques can broadly be categorised as manual (human) inspection or automated inspection. Human techniques are generally more time consuming than automated methods and studies have shown that even where the inspection process is structured and repeatable, manual inspection is only approximately 80% accurate [1]. Moreover, the time consuming nature of manual inspection is prohibitive in commercial printing applications where typically a printing press may operate at speeds in excess of two meters per second, necessitating fast inspection of printed images. Clearly, such inspection rates are beyond human capability.
Generally, automated inspection systems fall into one of three categories depending on the defect detection approach: (i) image reference (or template matching) approaches, (ii) design rule approaches, or (iii) some combination of both (hybrid approaches) [2-5]. In the simplest image reference approach, a reference exists that allows a direct comparison between a potentially defective image and a corresponding reference image. It is typical in this case to inspect 100% of the potentially defective image. A more elaborate referential approach involves recognizing features of potentially defective items in images and comparing those features with a set of idealized or perfect features. Inspection coverage on potentially defective items can vary in this case and may not necessarily be 100%. In the design-rule approach, a set of rules that describe properties of images are defined and can be statistically verified for a potentially defective image. In this case, as little as 10% of a product need be inspected before generating the appropriate statistics and determining whether a defect exists.
Automated inspection methods require substantial computational resources, and this requirement is exacerbated where variable data prints must be inspected for image defects. In variable data printing each image can be different and, if a referential approach is adopted, each image must be inspected in relation to a different reference image. For example, a customer job may require personalization of each print with a different name, address, or other information. In some applications it may be necessary to conduct inspection of all printed images (e.g. in the pharmaceutical industry 100% inspection is required for medicine labels). Such automated inspection methods cause either significant delay, or increase in costs.
In order to meet the demand for automated defect detection of variable data products, time and/or cost efficient image detection methods and apparatus are desirable.